The collection of distributions to be calculated by a {@link com.bayesserver.inference.IInference#query}. Only request those that you need.
If required, a distribution collection can be dynamically attached or detached from an {@link com.bayesserver.inference.IInference inference engine}. Also note that individual elements can be enabled or disabled on a per query basis.
Each distribution added, such as a {@link com.bayesserver.Table}, acts like a buffer. I.e. the values are populated by the {@link com.bayesserver.inference.IInference#query} method. This means that the same distributions can be re-used across multiple calls to {@link com.bayesserver.inference.IInference#query}, without the need to recreate the distribution each time.
It is important not to request distributions you do not need, because the computations will take longer. For example, it is common to request all marginal probabilities, P(A), P(B), P(C), ... , P(Z) given the {@link com.bayesserver.inference.IEvidence evidence}, however if you only need to know P(A) and P(B) given the {@link com.bayesserver.inference.IEvidence evidence}, then limit the query to just these distributions.
Typically the distributions requested are a number of marginal probabilities, such as P(A), P(B), P(C) etc... given the {@link com.bayesserver.inference.IEvidence evidence}, however it is also possible to request distributions over more than one variable, so we might query P(A), P(B,C) given the {@link com.bayesserver.inference.IEvidence evidence}.
If you are performing a batch of queries, there is no need to remove distributions that happen to have evidence set for a particular query.
For Dynamic Bayesian networks, times can be associated with distribution variables, to predict values in the future, present or past.
The collection of distributions to be calculated by a {@link com.bayesserver.inference.IInference#query}. Only request those that you need.
If required, a distribution collection can be dynamically attached or detached from an {@link com.bayesserver.inference.IInference inference engine}. Also note that individual elements can be enabled or disabled on a per query basis.
Each distribution added, such as a {@link com.bayesserver.Table}, acts like a buffer. I.e. the values are populated by the {@link com.bayesserver.inference.IInference#query} method. This means that the same distributions can be re-used across multiple calls to {@link com.bayesserver.inference.IInference#query}, without the need to recreate the distribution each time.
It is important not to request distributions you do not need, because the computations will take longer. For example, it is common to request all marginal probabilities, P(A), P(B), P(C), ... , P(Z) given the {@link com.bayesserver.inference.IEvidence evidence}, however if you only need to know P(A) and P(B) given the {@link com.bayesserver.inference.IEvidence evidence}, then limit the query to just these distributions.
Typically the distributions requested are a number of marginal probabilities, such as P(A), P(B), P(C) etc... given the {@link com.bayesserver.inference.IEvidence evidence}, however it is also possible to request distributions over more than one variable, so we might query P(A), P(B,C) given the {@link com.bayesserver.inference.IEvidence evidence}.
If you are performing a batch of queries, there is no need to remove distributions that happen to have evidence set for a particular query.
For Dynamic Bayesian networks, times can be associated with distribution variables, to predict values in the future, present or past.